Magnify Shopping

Built something that addresses a gap I kept hitting with LangChain agents in production:

policy enforcement at the tool-call level.

The problem: instructions in system prompts are only as reliable as the model's adherence

to them. When context grows, when prompts are injected, or when the model hallucinates —

the rules get ignored. The tool fires anyway.

SupraWall wraps your LangChain agent and intercepts every tool call before it executes.

Policy is deterministic — defined in code, evaluated outside the LLM. Same input, same

outcome, every time.

```python

from suprawall import secure_agent

from my_langchain_app import build_agent

agent = secure_agent(build_agent())

result = agent.invoke({"input": "Delete old records"})

# Tool calls intercepted and evaluated before execution

```

Works with LangGraph too. Every decision is RSA-signed and logged.

GitHub: https://github.com/wiserautomation/SupraWall

LangChain integration docs: https://supra-wall.com/docs/langchain

Would love to hear from anyone running LangChain agents in regulated environments —

specifically interested in edge cases with LangGraph's multi-step agent loops.